ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation

Deep neural networks are vulnerable to adversarial attacks. The literature is rich with algorithms that can easily craft successful adversarial examples. In contrast, the performance of defense techniques still lags behind. This paper proposes ME-Net, a defense method that leverages matrix estimation (ME). In ME-Net, images are preprocessed using two steps: first pixels are randomly dropped from the image; then, the image is reconstructed using ME. We show that this process destroys the adversarial structure of the noise, while re-enforcing the global structure in the original image. Since humans typically rely on such global structures in classifying images, the process makes the network mode compatible with human perception. We conduct comprehensive experiments on prevailing benchmarks such as MNIST, CIFAR-10, SVHN, and Tiny-ImageNet. Comparing ME-Net with state-of-the-art defense mechanisms shows that ME-Net consistently outperforms prior techniques, improving robustness against both black-box and white-box attacks.Comment: ICML 201

Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing

Given significant air pollution problems, air quality index (AQI) monitoring has recently received increasing attention. In this paper, we design a mobile AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS, to efficiently build fine-grained AQI maps in realtime. Specifically, we first propose the Gaussian plume model on basis of the neural network (GPM-NN), to physically characterize the particle dispersion in the air. Based on GPM-NN, we propose a battery efficient and adaptive monitoring algorithm to monitor AQI at the selected locations and construct an accurate AQI map with the sensed data. The proposed adaptive monitoring algorithm is evaluated in two typical scenarios, a two-dimensional open space like a roadside park, and a three-dimensional space like a courtyard inside a building. Experimental results demonstrate that our system can provide higher prediction accuracy of AQI with GPM-NN than other existing models, while greatly reducing the power consumption with the adaptive monitoring algorithm

On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond

Real-world data often exhibit imbalanced label distributions. Existing studies on data imbalance focus on single-domain settings, i.e., samples are from the same data distribution. However, natural data can originate from distinct domains, where a minority class in one domain could have abundant instances from other domains. We formalize the task of Multi-Domain Long-Tailed Recognition (MDLT), which learns from multi-domain imbalanced data, addresses label imbalance, domain shift, and divergent label distributions across domains, and generalizes to all domain-class pairs. We first develop the domain-class transferability graph, and show that such transferability governs the success of learning in MDLT. We then propose BoDA, a theoretically grounded learning strategy that tracks the upper bound of transferability statistics, and ensures balanced alignment and calibration across imbalanced domain-class distributions. We curate five MDLT benchmarks based on widely-used multi-domain datasets, and compare BoDA to twenty algorithms that span different learning strategies. Extensive and rigorous experiments verify the superior performance of BoDA. Further, as a byproduct, BoDA establishes new state-of-the-art on Domain Generalization benchmarks, highlighting the importance of addressing data imbalance across domains, which can be crucial for improving generalization to unseen domains. Code and data are available at: https://github.com/YyzHarry/multi-domain-imbalance.Comment: ECCV 202

Targeting Downstream Effectors of IGF/Insulin Signaling System in Human Breast Cancer

University of Minnesota Ph.D. dissertation. August 2015. Major: Pharmacology. Advisor: Douglas Yee. 1 computer file (PDF); xv, 128 pages.Transmembrane growth factor receptors mediate signaling through multiple intracellular pathways. In breast cancer cells, the type I insulin-like growth factor receptor (IGF-IR) has been implicated of tumorigenicity, proliferation, and metastasis. However, clinical trials with anti-IGF-IR monoclonal antibodies have generally been disappointing, partially due to lack of predictive biomarkers or adaptive compensational pathways activated when IGF-IR is blocked. To determine whether IGF-IR inhibition could be enhanced by disrupting other pathways, here we sought to investigate novel molecular targets downstream of IGF-IR signaling system and evaluate combination efficacy in estrogen receptor (ER) positive, basal-like, and endocrine resistant human breast cancer cell lines in vitro. The first part of this dissertation focuses on characterization of the mechanism of action and evaluation of therapeutic efficacy of a novel insulin receptor substrate 1 and 2 (IRS1/2) targeting compound NT157 in multiple breast cancer types. IRS1/2 transduce signaling from IGF-IR and insulin receptor (InR) to mediate the IGF effects on breast cancer cell biology. IRS-1 plays a critical role in cancer cell proliferation in ER positive breast cancers while IRS-2 is the predominate isoform in many basal-like breast cancers and is associated with motility and metastasis. NT157, a small-molecule tyrphostin, has been shown to downregulate IRS proteins in several model systems. In ER positive and basal-like breast cancer cells, NT157 treatment suppressed IRS protein expression in a dose dependent manner. NT157 treatment did not affect IGF-I, IGF-II, and insulin induced activation of phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) and mitogen-activated protein kinase (MAPK) in the short term, but longer exposure to NT157 inhibited the activation of these signaling pathways. The ability of NT157 to induce serine phosphorylation of IRS proteins was dependent on MAPK activation. Serine phosphorylation resulted in disassociation between IRS proteins and their receptors resulting in IRS degradation. In ER positive breast cancer cells, NT157 also resulted in cytoplasmic ERα downregulation likely because of disruption of an IRS-1-IGF-IR/InR/ERα complex. NT157 decreased S phase fraction after IGF/insulin treatment in ER positive breast cancer cells with inhibition of monolayer and anchorage-independent growth. NT157 downregulation of IRS protein expression also sensitized ER positive breast cancer cells to rapamycin. Moreover, NT157 inhibited the growth of tamoxifen resistant ER positive breast cancer cells. In basal-like breast cancer cells, NT157 repressed the proliferation (G2/M abrogation) and migration through downregulation of IRS1/2 protein. Given that both IGF-IR and InR play a role in cancer biology, targeting of IRS adaptor proteins may be a more effective strategy to inhibit these receptors. In the second part of this dissertation, we highlight an amino acid transporter �" xC- to be a novel co-target in addition to IGF-IR targeted therapies in ER positive breast cancer cells. IGF-I stimulates growth of normal and malignant cells. Increased uptake of amino acids after activation of IGF-IR signaling has been well characterized. xCT (SLC7A11) encodes the functional subunit of the heterodimeric plasma membrane transport system xC- critical for the cellular uptake of cystine, generation of glutathione, and modulation of cellular redox control. Here, we show that IGF-I induced xCT mRNA, protein expression, and function in ER positive breast cancer cell lines in an IRS-1 dependent manner. IGF-I further controlled cellular redox level through the xC- transporter. IGF-I-stimulated monolayer and anchorage-independent growth was suppressed by reducing xCT expression or by treating cells with the xC- chemical inhibitor sulfasalazine (SASP). Anchorage-independent growth assays showed that disruption of xC- function by SASP sensitized cells to anti-IGF-IR inhibitors (monoclonal antibody huEM164 and tyrosine kinase inhibitor NVP-AEW-541). The growth suppressive effects of SASP were reversed by the ROS scavenger N-acetyl-L-cysteine. Thus, IGF-I promotes the proliferation of ER positive breast cancer cells by regulating xC- transporter function to protect cancer cells from ROS in an IRS-1 dependent manner. Our findings also imply that inhibition of xC- transporter function combined with anti-IGF-IR agents may have synergistic therapeutic effect. The third part of this dissertation aims at thoroughly investigating the IGF’s regulation on Nuclear factor-erythroid 2-related factor 2 (Nrf2) in ER+ breast cancers and evaluating Nrf2 as a target in triple negative / basal-like (TNBC) breast cancers. Nrf2 is a key transcriptional activator that mediates cellular antioxidant response by initiating expression of various anti-oxidative and anti-inflammation genes. Constitutive stabilization of Nrf2 has been observed in many human cancers and confers chemo- and radio-resistance of cancer cells. We examined Nrf2 expression and function in a panel of breast cancer cell lines. mRNA expression of Nrf2 was higher in the TNBC/basal-like cell lines MDA-MB-231 and MDA-MB 436 compared to immortalized breast epithelial cells and other types of breast cancers. In estrogen receptor positive (ER+) breast cancer cells MCF-7 and T47D where basal level of Nrf2 were low, IGF signaling system regulated Nrf2 expression, nucleus translocation and ARE-binding capability. Downregulation of Nrf2 sensitized ER+ cells’ response towards irradiation in the presence of IGF-I ligand. shRNA knock-down of Nrf2 in MDA-MB-231 and MDA-MB-436 TNBC cells showed decreased mRNA expression of multiple Nrf2 regulated anti-oxidant and pentose phosphate pathway genes, enhanced basal levels of cellular reactive oxygen species, impaired mitochondrial function and reduced S phase entry. Cells with decreased Nrf2 had reduced cell growth in monolayer, anchorage independent, and 3-dimensional growth assays. In addition, Nrf2 suppression reduced cell migration. Nrf2 down-regulated MDA-MB-231 cells also showed increased response towards ionizing radiation in clonogenic and soft agar assays. Furthermore, reduced Nrf2 expression decreased the number of stem-like (CD44+/CD24-) population in MDA-MB-231 cells possibly through xC- transporter regulation. Thus, IGF signaling induces Nrf2 expression and function, which suggests Nrf2 could be a therapeutic co-target in combination to anti-IGF treatment. Nrf2 regulates various aspects of the malignant phenotype in TNBC that inhibition of Nrf2 might be a therapeutic option for TNBC. Taken together, the data in this thesis demonstrate that IGF-IR activation stimulates multiple downstream effectors important for breast cancer cell biology. Inhibition of selected downstream signaling molecules alone or in combination with anti-IGF-IR drugs is likely to better therapeutic outcomes in breast cancers

Change is Hard: A Closer Look at Subpopulation Shift

Machine learning models often perform poorly on subgroups that are underrepresented in the training data. Yet, little is understood on the variation in mechanisms that cause subpopulation shifts, and how algorithms generalize across such diverse shifts at scale. In this work, we provide a fine-grained analysis of subpopulation shift. We first propose a unified framework that dissects and explains common shifts in subgroups. We then establish a comprehensive benchmark of 20 state-of-the-art algorithms evaluated on 12 real-world datasets in vision, language, and healthcare domains. With results obtained from training over 10,000 models, we reveal intriguing observations for future progress in this space. First, existing algorithms only improve subgroup robustness over certain types of shifts but not others. Moreover, while current algorithms rely on group-annotated validation data for model selection, we find that a simple selection criterion based on worst-class accuracy is surprisingly effective even without any group information. Finally, unlike existing works that solely aim to improve worst-group accuracy (WGA), we demonstrate the fundamental tradeoff between WGA and other important metrics, highlighting the need to carefully choose testing metrics. Code and data are available at: https://github.com/YyzHarry/SubpopBench.Comment: ICML 202